Unnatural L0 Sparse Representation for Natural Image Deblurring
We show in this paper that the success of previous maximum a posterior (MAP) based blur removal methods partly stems from their respective intermediate steps, which implicitly or explicitly create an unnatural representation containing salient image structures. We propose a generalized and mathematically sound L 0 sparse expression, together with a new effective method, for motion deblurring. Our system does not require extra filtering during optimization and demonstrates fast energy decreasing, making a small number of iterations enough for convergence. It also provides a unified framework for both uniform and non-uniform motion deblurring. We extensively validate our method and show comparison with other approaches with respect to convergence speed, running time, and result quality.
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Ranked #13 on Deblurring on RealBlur-R (trained on GoPro) (SSIM (sRGB) metric)
Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
---|---|---|---|---|---|---|
Deblurring | RealBlur-R (trained on GoPro) | Xu et al | SSIM (sRGB) | 0.937 | # 13 |